Construction of a diagnostic prediction model for ischemic stroke using lactylation - related genes

Jierong Mo^1*Deyuan Ning¹Shuyi Chen²Tianen Zhou²Peiyi Liu²Zhiquan Li²WenMin Tang²Guo Lianshan²Zhiyong Lin²Ran Zhan²Jun Jiang²Xu Li¹

• ¹Southern Medical University Nanfang Hospital, Guangzhou, China
• ²First People's Hospital of Foshan, Foshan, China

Ischemic stroke (IS) represents the leading global cause of acquired neurological disability and vascular-related mortality. However, diagnostic challenges persist in cases with atypical presentations. Lactylation modification exerts critical regulatory roles in disease pathogenesis and progression, and thus serves as a potential diagnostic biomarker. Weighted Gene Co - expression Network Analysis (WGCNA), Gene Ontology (GO)/ Kyoto Encyclopedia of Genes and Genomes (KEGG), Immune infiltration, consensusClusterPlus, machine learning algorithms (including Random Forest , Support Vector Machine, Neural Network and Generalized Linear Models), RT-qPCR and western blot were used to analyze Gene Expression Omnibus (GEO) datasets. Our findings indicate that immune infiltration may play an important role in IS, with neutrophils and T cell receptor signaling pathway identified as the most important immune cells and signaling pathway, respectively. Six hub genes (SLC2A3, NDUFB11, GTPBP3, SLC16A3, PUS1, and GRN) were identified and were verified through RT-qPCR and western blot. Surprisingly, the AUC area of the prediction model reached 0.968, with a 95% confidence interval of [0.928,1]. Extensive validation using multiple external GEO datasets confirmed the accuracy of the prediction model in five independent datasets. Finally, we observed that different concentrations of lactate could further suppress the proliferation of nerve cells following OGD/R. This study provides a new diagnostic strategy for the early diagnosis of IS through the established diagnostic prediction model.